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Hmm? That's not my takeaway from the tweet (xeet?). He's not denying a connection between AI capabilities and code quality decline, he's making a more subtle point about skill distribution.
The basic model goes like this: AI tools multiply your output at every skill level. Give a novice programmer access to ChatGPT, Claude or Copilot (maybe not Copilot, lol) , and they go from "can't write working code" to "can produce something that technically runs." Give an expert like Karpathy the same tools, and he goes from "excellent" to "somewhat more excellent." The multiplicative factor might even be similar! But that's the rub, there are way more novices than experts.
So you get a flood of new code. Most of it is mediocre-to-bad, because most people are mediocre-to-bad at programming, and multiplying "bad" by even a generous factor still gives you "not great." The experts are also producing more, and their output is better, but nobody writes news articles about the twentieth high-quality library that quietly does its job. We only notice when things break.
This maps onto basically every domain. Take medicine as a test case (yay, the one domain where I'm a quasi-expert) Any random person can feed their lab results into ChatGPT and get something interpretable back. This is genuinely useful! Going from "incomprehensible numbers" to "your kidneys are probably fine but your cholesterol needs work" is a huge upgrade for the average patient. They might miss nuances or accept hallucinated explanations, but they're still better off than before.
Meanwhile, as someone who actually understands medicine, I can extract even more value. I can write better prompts, catch inconsistencies, verify citations, and integrate the AI's suggestions into a broader clinical picture. The AI makes me more productive, but I was already productive, so the absolute gains are smaller relative to my baseline. And critically, I'm less likely to get fooled by confident-sounding nonsense (it's rare but happens at above negligible rates).
This is where I tentatively endorse a "skill issue" framing, where everyone's output getting multiplied, but bad times a multiplier is still usually bad, and there are simply more bad actors (in the neutral sense) than good ones. The denominator in "slop per good output" has gotten larger, but so has the numerator, and the numerator was already bigger to start with. From inside the system, if you're Andrej Karpathy, you mostly notice that you're faster. From outside, you notice that GitHub is full of garbage and the latest Windows update broke your system.
This isn't even a new pattern. Every productivity tool follows similar dynamics. When word processors became common, suddenly everyone could produce professional-looking documents. Did the average quality of written work improve? Well, the floor certainly rose (less illegible handwriting, if I continue to accurately insult my colleagues), but we also got an explosion of mediocre memos and reports that previously wouldn't have been written at all. The ceiling barely budged because good writers were already good. I get more use out of an LLM for writing advice than, say, Scott.
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